"""
管理层智能体

实现管理执行层的智能体，包括5个总监角色。
"""

from typing import Dict, List, Any, Optional
from datetime import datetime, timedelta
from dataclasses import dataclass
from enum import Enum

from .base_agent import BaseAgent
from .agent_roles import AgentRole, AgentCapability
from ..coordination.coordination_patterns import CoordinationContext, CoordinationMode


class TaskPriority(Enum):
    """任务优先级"""
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    URGENT = "urgent"


@dataclass
class ManagementTask:
    """管理任务"""
    task_id: str
    task_type: str
    description: str
    priority: TaskPriority
    assigned_to: List[str]
    deadline: datetime
    status: str = "pending"  # pending, in_progress, completed, cancelled
    progress: float = 0.0
    dependencies: List[str] = None
    resources_required: Dict[str, Any] = None
    created_by: str = ""
    created_at: datetime = None
    
    def __post_init__(self):
        if self.dependencies is None:
            self.dependencies = []
        if self.resources_required is None:
            self.resources_required = {}
        if self.created_at is None:
            self.created_at = datetime.now()


class ManagementAgent(BaseAgent):
    """管理层智能体基类"""
    
    def __init__(self, agent_id: str, role: AgentRole):
        """
        初始化管理层智能体
        
        Args:
            agent_id: 智能体ID
            role: 智能体角色
        """
        super().__init__(agent_id, role)
        self._managed_tasks: List[ManagementTask] = []
        self._team_members: List[str] = []
        self._performance_metrics: Dict[str, float] = {}
        self._resource_allocation: Dict[str, Any] = {}
    
    async def assign_task(
        self,
        task_type: str,
        description: str,
        assignees: List[str],
        priority: TaskPriority = TaskPriority.MEDIUM,
        deadline: Optional[datetime] = None
    ) -> ManagementTask:
        """
        分配任务
        
        Args:
            task_type: 任务类型
            description: 任务描述
            assignees: 被分配人列表
            priority: 任务优先级
            deadline: 截止时间
            
        Returns:
            管理任务
        """
        if deadline is None:
            deadline = datetime.now() + timedelta(days=7)
        
        task = ManagementTask(
            task_id=f"MT_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            task_type=task_type,
            description=description,
            priority=priority,
            assigned_to=assignees,
            deadline=deadline,
            created_by=self.agent_id
        )
        
        self._managed_tasks.append(task)
        return task
    
    async def monitor_progress(self) -> Dict[str, Any]:
        """
        监控进度
        
        Returns:
            进度报告
        """
        total_tasks = len(self._managed_tasks)
        completed_tasks = len([t for t in self._managed_tasks if t.status == "completed"])
        in_progress_tasks = len([t for t in self._managed_tasks if t.status == "in_progress"])
        overdue_tasks = len([
            t for t in self._managed_tasks 
            if t.deadline < datetime.now() and t.status != "completed"
        ])
        
        progress_report = {
            "report_id": f"PR_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "manager": self.agent_id,
            "total_tasks": total_tasks,
            "completed_tasks": completed_tasks,
            "in_progress_tasks": in_progress_tasks,
            "overdue_tasks": overdue_tasks,
            "completion_rate": completed_tasks / total_tasks if total_tasks > 0 else 0,
            "team_performance": self._calculate_team_performance(),
            "resource_utilization": self._calculate_resource_utilization(),
            "recommendations": self._generate_recommendations()
        }
        
        return progress_report
    
    def _calculate_team_performance(self) -> Dict[str, float]:
        """计算团队绩效"""
        # 基础绩效计算逻辑
        return {
            "efficiency": 0.85,
            "quality": 0.90,
            "collaboration": 0.88,
            "innovation": 0.82
        }
    
    def _calculate_resource_utilization(self) -> Dict[str, float]:
        """计算资源利用率"""
        return {
            "human_resources": 0.80,
            "computational_resources": 0.75,
            "data_resources": 0.90,
            "financial_resources": 0.85
        }
    
    def _generate_recommendations(self) -> List[str]:
        """生成改进建议"""
        return [
            "优化任务分配策略",
            "加强团队协作",
            "提升资源利用效率",
            "完善绩效评估体系"
        ]


class InvestmentDirectorAgent(ManagementAgent):
    """投资总监智能体"""
    
    def __init__(self, agent_id: str = "inv_dir_001"):
        """初始化投资总监智能体"""
        super().__init__(agent_id, AgentRole.INVESTMENT_DIRECTOR)
        self._investment_strategies = {}
        self._portfolio_managers = []
        self._performance_targets = {}
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        处理任务
        
        Args:
            task: 任务信息
            
        Returns:
            处理结果
        """
        task_type = task.get("type", "")
        
        if task_type == "strategy_implementation":
            return await self._implement_strategy(task)
        elif task_type == "portfolio_oversight":
            return await self._oversee_portfolios(task)
        elif task_type == "performance_review":
            return await self._review_performance(task)
        elif task_type == "team_management":
            return await self._manage_investment_team(task)
        else:
            return {"status": "error", "message": f"未知任务类型: {task_type}"}
    
    async def _implement_strategy(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """实施投资策略"""
        strategy = task.get("strategy", {})
        
        implementation_plan = {
            "plan_id": f"IP_{datetime.now().strftime('%Y%m%d')}",
            "strategy_id": strategy.get("strategy_id"),
            "implementation_phases": [
                {
                    "phase": "准备阶段",
                    "duration": "1周",
                    "activities": ["团队培训", "系统配置", "风险评估"]
                },
                {
                    "phase": "执行阶段",
                    "duration": "4周",
                    "activities": ["资产配置调整", "投资组合构建", "风险监控"]
                },
                {
                    "phase": "监控阶段",
                    "duration": "持续",
                    "activities": ["绩效跟踪", "风险管理", "策略优化"]
                }
            ],
            "resource_requirements": {
                "analysts": 3,
                "portfolio_managers": 2,
                "risk_managers": 1,
                "budget": 1000000
            },
            "success_metrics": {
                "target_return": 0.12,
                "max_drawdown": 0.08,
                "sharpe_ratio": 1.5
            }
        }
        
        # 分配实施任务
        await self.assign_task(
            "strategy_implementation",
            f"实施投资策略: {strategy.get('strategy_id')}",
            self._portfolio_managers,
            TaskPriority.HIGH,
            datetime.now() + timedelta(weeks=6)
        )
        
        return {
            "status": "success",
            "implementation_plan": implementation_plan
        }
    
    async def _oversee_portfolios(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """监督投资组合"""
        portfolios = task.get("portfolios", [])
        
        oversight_report = {
            "report_id": f"OR_{datetime.now().strftime('%Y%m%d')}",
            "portfolios_reviewed": len(portfolios),
            "key_findings": [
                "整体表现符合预期",
                "风险控制有效",
                "部分组合需要调整"
            ],
            "performance_summary": {
                "average_return": 0.08,
                "best_performer": "growth_portfolio",
                "worst_performer": "value_portfolio",
                "risk_adjusted_return": 0.65
            },
            "action_items": [
                "调整低表现组合配置",
                "加强风险监控",
                "优化交易执行"
            ]
        }
        
        return {
            "status": "success",
            "oversight_report": oversight_report
        }
    
    async def _review_performance(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """审查绩效表现"""
        period = task.get("period", "月度")
        
        performance_review = {
            "review_id": f"PFR_{datetime.now().strftime('%Y%m%d')}",
            "review_period": period,
            "overall_performance": {
                "return": 0.085,
                "benchmark_return": 0.075,
                "alpha": 0.01,
                "beta": 0.95,
                "information_ratio": 0.8
            },
            "attribution_analysis": {
                "asset_allocation": 0.003,
                "security_selection": 0.005,
                "interaction": 0.002
            },
            "risk_metrics": {
                "volatility": 0.12,
                "var_95": 0.025,
                "max_drawdown": 0.06
            },
            "recommendations": [
                "继续执行当前策略",
                "适度增加成长股配置",
                "加强下行风险保护"
            ]
        }
        
        return {
            "status": "success",
            "performance_review": performance_review
        }
    
    async def _manage_investment_team(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """管理投资团队"""
        team_action = task.get("action", "")
        
        if team_action == "performance_evaluation":
            evaluation = {
                "evaluation_id": f"TE_{datetime.now().strftime('%Y%m%d')}",
                "team_members": self._team_members,
                "evaluation_criteria": [
                    "投资业绩",
                    "风险管理",
                    "团队协作",
                    "专业发展"
                ],
                "overall_rating": "优秀",
                "development_plans": [
                    "加强量化分析技能",
                    "提升ESG投资能力",
                    "完善风险管理知识"
                ]
            }
            return {"status": "success", "evaluation": evaluation}
        
        elif team_action == "resource_allocation":
            allocation = {
                "allocation_id": f"RA_{datetime.now().strftime('%Y%m%d')}",
                "human_resources": {
                    "senior_analysts": 2,
                    "junior_analysts": 4,
                    "portfolio_managers": 3
                },
                "budget_allocation": {
                    "research_tools": 200000,
                    "data_subscriptions": 150000,
                    "training_development": 100000
                },
                "technology_resources": {
                    "trading_systems": "已配置",
                    "analytics_platforms": "已配置",
                    "risk_systems": "需要升级"
                }
            }
            return {"status": "success", "allocation": allocation}
        
        return {"status": "error", "message": f"未知团队管理操作: {team_action}"}


class RiskDirectorAgent(ManagementAgent):
    """风险总监智能体"""
    
    def __init__(self, agent_id: str = "risk_dir_001"):
        """初始化风险总监智能体"""
        super().__init__(agent_id, AgentRole.RISK_DIRECTOR)
        self._risk_policies = {}
        self._risk_limits = {}
        self._monitoring_systems = []
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        处理任务
        
        Args:
            task: 任务信息
            
        Returns:
            处理结果
        """
        task_type = task.get("type", "")
        
        if task_type == "risk_assessment":
            return await self._assess_risk(task)
        elif task_type == "limit_monitoring":
            return await self._monitor_limits(task)
        elif task_type == "stress_testing":
            return await self._conduct_stress_test(task)
        elif task_type == "risk_reporting":
            return await self._generate_risk_report(task)
        else:
            return {"status": "error", "message": f"未知任务类型: {task_type}"}
    
    async def _assess_risk(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """评估风险"""
        assessment_scope = task.get("scope", "portfolio")
        
        risk_assessment = {
            "assessment_id": f"RA_{datetime.now().strftime('%Y%m%d')}",
            "scope": assessment_scope,
            "risk_categories": {
                "market_risk": {
                    "level": "中等",
                    "var_95": 0.025,
                    "expected_shortfall": 0.035
                },
                "credit_risk": {
                    "level": "低",
                    "default_probability": 0.002,
                    "exposure_at_default": 0.6
                },
                "liquidity_risk": {
                    "level": "低",
                    "liquidity_ratio": 0.85,
                    "funding_gap": 0.05
                },
                "operational_risk": {
                    "level": "中等",
                    "key_risk_indicators": ["系统故障", "人员流失", "合规违规"]
                }
            },
            "overall_risk_rating": "中等",
            "recommendations": [
                "加强市场风险监控",
                "完善流动性管理",
                "提升操作风险控制"
            ]
        }
        
        return {
            "status": "success",
            "risk_assessment": risk_assessment
        }
    
    async def _monitor_limits(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """监控限额"""
        monitoring_report = {
            "report_id": f"LM_{datetime.now().strftime('%Y%m%d')}",
            "monitoring_date": datetime.now(),
            "limit_status": {
                "var_limit": {
                    "limit": 0.02,
                    "current": 0.018,
                    "utilization": 0.90,
                    "status": "正常"
                },
                "concentration_limit": {
                    "limit": 0.10,
                    "current": 0.08,
                    "utilization": 0.80,
                    "status": "正常"
                },
                "leverage_limit": {
                    "limit": 2.0,
                    "current": 1.5,
                    "utilization": 0.75,
                    "status": "正常"
                }
            },
            "breaches": [],
            "warnings": [
                "VaR接近限额，需要关注"
            ],
            "actions_required": [
                "密切监控VaR变化",
                "准备风险缓释措施"
            ]
        }
        
        return {
            "status": "success",
            "monitoring_report": monitoring_report
        }
    
    async def _conduct_stress_test(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """进行压力测试"""
        scenarios = task.get("scenarios", ["市场崩盘", "利率冲击", "流动性危机"])
        
        stress_test_results = {
            "test_id": f"ST_{datetime.now().strftime('%Y%m%d')}",
            "test_date": datetime.now(),
            "scenarios_tested": scenarios,
            "results": {
                "市场崩盘": {
                    "portfolio_loss": -0.15,
                    "var_impact": 0.045,
                    "liquidity_impact": "中等"
                },
                "利率冲击": {
                    "portfolio_loss": -0.08,
                    "duration_risk": 0.025,
                    "credit_spread_impact": 0.015
                },
                "流动性危机": {
                    "funding_gap": 0.12,
                    "asset_liquidation_cost": 0.03,
                    "time_to_liquidity": "5天"
                }
            },
            "overall_resilience": "良好",
            "contingency_plans": [
                "启动风险缓释措施",
                "调整投资组合配置",
                "增加流动性缓冲"
            ]
        }
        
        return {
            "status": "success",
            "stress_test_results": stress_test_results
        }
    
    async def _generate_risk_report(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """生成风险报告"""
        report_type = task.get("report_type", "daily")
        
        risk_report = {
            "report_id": f"RR_{datetime.now().strftime('%Y%m%d')}",
            "report_type": report_type,
            "report_date": datetime.now(),
            "executive_summary": "整体风险水平可控，需要关注市场波动性上升",
            "key_metrics": {
                "portfolio_var": 0.018,
                "expected_shortfall": 0.025,
                "maximum_drawdown": 0.06,
                "beta": 0.95
            },
            "risk_trends": {
                "market_risk": "上升",
                "credit_risk": "稳定",
                "liquidity_risk": "稳定",
                "operational_risk": "稳定"
            },
            "alerts": [
                "市场波动性增加",
                "部分资产流动性下降"
            ],
            "recommendations": [
                "适度降低风险敞口",
                "增加对冲策略",
                "提高现金比例"
            ]
        }
        
        return {
            "status": "success",
            "risk_report": risk_report
        }


class OperationsDirectorAgent(ManagementAgent):
    """运营总监智能体"""
    
    def __init__(self, agent_id: str = "ops_dir_001"):
        """初始化运营总监智能体"""
        super().__init__(agent_id, AgentRole.OPERATIONS_DIRECTOR)
        self._operational_processes = {}
        self._system_status = {}
        self._service_levels = {}
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        处理任务
        
        Args:
            task: 任务信息
            
        Returns:
            处理结果
        """
        task_type = task.get("type", "")
        
        if task_type == "system_monitoring":
            return await self._monitor_systems(task)
        elif task_type == "process_optimization":
            return await self._optimize_processes(task)
        elif task_type == "service_management":
            return await self._manage_services(task)
        elif task_type == "incident_response":
            return await self._respond_to_incident(task)
        else:
            return {"status": "error", "message": f"未知任务类型: {task_type}"}
    
    async def _monitor_systems(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """监控系统"""
        monitoring_report = {
            "report_id": f"SM_{datetime.now().strftime('%Y%m%d_%H%M')}",
            "monitoring_time": datetime.now(),
            "system_status": {
                "trading_system": {
                    "status": "正常",
                    "uptime": 0.999,
                    "response_time": "50ms",
                    "throughput": "1000 TPS"
                },
                "data_system": {
                    "status": "正常",
                    "data_quality": 0.98,
                    "latency": "100ms",
                    "coverage": 0.95
                },
                "risk_system": {
                    "status": "正常",
                    "calculation_accuracy": 0.999,
                    "update_frequency": "实时",
                    "alert_response": "5秒"
                },
                "reporting_system": {
                    "status": "正常",
                    "report_generation": "按时",
                    "data_accuracy": 0.99,
                    "delivery_rate": 0.98
                }
            },
            "performance_metrics": {
                "overall_availability": 0.998,
                "average_response_time": "75ms",
                "error_rate": 0.001,
                "user_satisfaction": 0.92
            },
            "alerts": [],
            "recommendations": [
                "优化数据库查询性能",
                "升级网络基础设施",
                "加强系统监控"
            ]
        }
        
        return {
            "status": "success",
            "monitoring_report": monitoring_report
        }
    
    async def _optimize_processes(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """优化流程"""
        process_name = task.get("process", "")
        
        optimization_plan = {
            "plan_id": f"OP_{datetime.now().strftime('%Y%m%d')}",
            "process_name": process_name,
            "current_metrics": {
                "cycle_time": "2小时",
                "error_rate": 0.02,
                "cost_per_transaction": 50,
                "customer_satisfaction": 0.85
            },
            "optimization_targets": {
                "cycle_time": "1小时",
                "error_rate": 0.01,
                "cost_per_transaction": 35,
                "customer_satisfaction": 0.90
            },
            "improvement_initiatives": [
                "自动化手工流程",
                "简化审批环节",
                "优化系统集成",
                "加强质量控制"
            ],
            "implementation_timeline": "3个月",
            "expected_benefits": {
                "efficiency_gain": 0.30,
                "cost_reduction": 0.25,
                "quality_improvement": 0.15
            }
        }
        
        return {
            "status": "success",
            "optimization_plan": optimization_plan
        }
    
    async def _manage_services(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """管理服务"""
        service_management = {
            "management_id": f"SV_{datetime.now().strftime('%Y%m%d')}",
            "service_catalog": [
                "交易执行服务",
                "数据分析服务",
                "风险管理服务",
                "报告生成服务"
            ],
            "service_levels": {
                "availability": 0.995,
                "response_time": "< 100ms",
                "throughput": "> 500 TPS",
                "error_rate": "< 0.1%"
            },
            "current_performance": {
                "availability": 0.998,
                "response_time": "85ms",
                "throughput": "750 TPS",
                "error_rate": "0.05%"
            },
            "service_improvements": [
                "增加服务冗余",
                "优化负载均衡",
                "完善监控告警",
                "提升故障恢复能力"
            ]
        }
        
        return {
            "status": "success",
            "service_management": service_management
        }
    
    async def _respond_to_incident(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """响应事件"""
        incident = task.get("incident", {})
        
        incident_response = {
            "response_id": f"IR_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "incident_id": incident.get("incident_id"),
            "severity": incident.get("severity", "medium"),
            "response_team": [
                "运营工程师",
                "系统管理员",
                "技术支持"
            ],
            "response_actions": [
                "评估影响范围",
                "启动应急预案",
                "通知相关人员",
                "实施修复措施",
                "监控恢复状态"
            ],
            "estimated_resolution_time": "2小时",
            "communication_plan": {
                "internal_updates": "每30分钟",
                "external_communication": "每小时",
                "stakeholder_notification": "立即"
            },
            "lessons_learned": [
                "加强预防性维护",
                "完善监控覆盖",
                "优化响应流程"
            ]
        }
        
        return {
            "status": "success",
            "incident_response": incident_response
        }


class TechnologyDirectorAgent(ManagementAgent):
    """技术总监智能体"""
    
    def __init__(self, agent_id: str = "tech_dir_001"):
        """初始化技术总监智能体"""
        super().__init__(agent_id, AgentRole.TECHNOLOGY_DIRECTOR)
        self._technology_stack = {}
        self._development_projects = []
        self._infrastructure_status = {}
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        处理任务
        
        Args:
            task: 任务信息
            
        Returns:
            处理结果
        """
        task_type = task.get("type", "")
        
        if task_type == "architecture_design":
            return await self._design_architecture(task)
        elif task_type == "technology_evaluation":
            return await self._evaluate_technology(task)
        elif task_type == "project_management":
            return await self._manage_projects(task)
        elif task_type == "infrastructure_planning":
            return await self._plan_infrastructure(task)
        else:
            return {"status": "error", "message": f"未知任务类型: {task_type}"}
    
    async def _design_architecture(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """设计架构"""
        requirements = task.get("requirements", {})
        
        architecture_design = {
            "design_id": f"AD_{datetime.now().strftime('%Y%m%d')}",
            "requirements": requirements,
            "architecture_patterns": [
                "微服务架构",
                "事件驱动架构",
                "分层架构",
                "CQRS模式"
            ],
            "technology_choices": {
                "backend": "Python/FastAPI",
                "database": "PostgreSQL/TimescaleDB",
                "cache": "Redis",
                "message_queue": "RabbitMQ",
                "monitoring": "Prometheus/Grafana",
                "deployment": "Docker/Kubernetes"
            },
            "scalability_considerations": [
                "水平扩展能力",
                "负载均衡策略",
                "数据分片方案",
                "缓存策略"
            ],
            "security_measures": [
                "身份认证授权",
                "数据加密传输",
                "API安全防护",
                "审计日志记录"
            ],
            "implementation_phases": [
                "核心服务开发",
                "数据层实现",
                "API网关部署",
                "监控系统集成"
            ]
        }
        
        return {
            "status": "success",
            "architecture_design": architecture_design
        }
    
    async def _evaluate_technology(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """评估技术"""
        technology = task.get("technology", "")
        
        evaluation = {
            "evaluation_id": f"TE_{datetime.now().strftime('%Y%m%d')}",
            "technology": technology,
            "evaluation_criteria": [
                "技术成熟度",
                "性能表现",
                "社区支持",
                "学习成本",
                "维护成本",
                "安全性"
            ],
            "scores": {
                "技术成熟度": 8.5,
                "性能表现": 9.0,
                "社区支持": 8.0,
                "学习成本": 7.0,
                "维护成本": 8.5,
                "安全性": 9.0
            },
            "overall_score": 8.3,
            "pros": [
                "性能优秀",
                "生态丰富",
                "文档完善",
                "安全可靠"
            ],
            "cons": [
                "学习曲线较陡",
                "部分功能复杂",
                "资源消耗较高"
            ],
            "recommendation": "推荐采用",
            "implementation_considerations": [
                "团队培训计划",
                "迁移策略制定",
                "风险缓解措施",
                "性能基准测试"
            ]
        }
        
        return {
            "status": "success",
            "evaluation": evaluation
        }
    
    async def _manage_projects(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """管理项目"""
        project_action = task.get("action", "")
        
        if project_action == "status_review":
            project_status = {
                "review_id": f"PS_{datetime.now().strftime('%Y%m%d')}",
                "active_projects": [
                    {
                        "project_id": "PROJ_001",
                        "name": "交易系统升级",
                        "status": "进行中",
                        "progress": 0.75,
                        "budget_utilization": 0.68,
                        "timeline": "按计划"
                    },
                    {
                        "project_id": "PROJ_002",
                        "name": "数据平台优化",
                        "status": "进行中",
                        "progress": 0.45,
                        "budget_utilization": 0.52,
                        "timeline": "轻微延迟"
                    }
                ],
                "resource_allocation": {
                    "developers": 8,
                    "architects": 2,
                    "testers": 3,
                    "devops": 2
                },
                "key_milestones": [
                    "系统设计完成",
                    "核心功能开发",
                    "集成测试",
                    "生产部署"
                ]
            }
            return {"status": "success", "project_status": project_status}
        
        elif project_action == "resource_planning":
            resource_plan = {
                "plan_id": f"RP_{datetime.now().strftime('%Y%m%d')}",
                "planning_horizon": "下季度",
                "resource_requirements": {
                    "technical_staff": 15,
                    "infrastructure_budget": 500000,
                    "software_licenses": 200000,
                    "training_budget": 100000
                },
                "capacity_analysis": {
                    "current_capacity": 0.85,
                    "projected_demand": 0.95,
                    "capacity_gap": 0.10,
                    "scaling_plan": "招聘2名高级开发工程师"
                }
            }
            return {"status": "success", "resource_plan": resource_plan}
        
        return {"status": "error", "message": f"未知项目管理操作: {project_action}"}
    
    async def _plan_infrastructure(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """规划基础设施"""
        infrastructure_plan = {
            "plan_id": f"IP_{datetime.now().strftime('%Y%m%d')}",
            "current_infrastructure": {
                "servers": 20,
                "storage": "100TB",
                "network_bandwidth": "10Gbps",
                "cloud_resources": "AWS/阿里云混合"
            },
            "capacity_planning": {
                "cpu_utilization": 0.70,
                "memory_utilization": 0.65,
                "storage_utilization": 0.80,
                "network_utilization": 0.45
            },
            "scaling_requirements": {
                "additional_servers": 5,
                "storage_expansion": "50TB",
                "network_upgrade": "20Gbps",
                "cloud_migration": "部分工作负载"
            },
            "technology_roadmap": [
                "容器化部署",
                "微服务架构",
                "云原生技术",
                "边缘计算"
            ],
            "investment_timeline": {
                "Q1": "服务器采购",
                "Q2": "存储扩容",
                "Q3": "网络升级",
                "Q4": "云迁移"
            }
        }
        
        return {
            "status": "success",
            "infrastructure_plan": infrastructure_plan
        }


class ComplianceDirectorAgent(ManagementAgent):
    """合规总监智能体"""
    
    def __init__(self, agent_id: str = "comp_dir_001"):
        """初始化合规总监智能体"""
        super().__init__(agent_id, AgentRole.COMPLIANCE_DIRECTOR)
        self._compliance_policies = {}
        self._regulatory_requirements = []
        self._audit_schedule = {}
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        处理任务
        
        Args:
            task: 任务信息
            
        Returns:
            处理结果
        """
        task_type = task.get("type", "")
        
        if task_type == "compliance_monitoring":
            return await self._monitor_compliance(task)
        elif task_type == "regulatory_update":
            return await self._handle_regulatory_update(task)
        elif task_type == "audit_management":
            return await self._manage_audit(task)
        elif task_type == "policy_review":
            return await self._review_policies(task)
        else:
            return {"status": "error", "message": f"未知任务类型: {task_type}"}
    
    async def _monitor_compliance(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """监控合规性"""
        compliance_report = {
            "report_id": f"CM_{datetime.now().strftime('%Y%m%d')}",
            "monitoring_date": datetime.now(),
            "compliance_areas": {
                "投资者适当性": {
                    "status": "合规",
                    "last_check": datetime.now(),
                    "issues": 0,
                    "recommendations": []
                },
                "信息披露": {
                    "status": "合规",
                    "last_check": datetime.now(),
                    "issues": 0,
                    "recommendations": ["完善披露时效性"]
                },
                "风险管理": {
                    "status": "合规",
                    "last_check": datetime.now(),
                    "issues": 1,
                    "recommendations": ["加强压力测试频率"]
                },
                "反洗钱": {
                    "status": "合规",
                    "last_check": datetime.now(),
                    "issues": 0,
                    "recommendations": []
                }
            },
            "overall_compliance_score": 0.95,
            "action_items": [
                "更新KYC流程",
                "完善交易监控",
                "加强员工培训"
            ]
        }
        
        return {
            "status": "success",
            "compliance_report": compliance_report
        }
    
    async def _handle_regulatory_update(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """处理监管更新"""
        regulation = task.get("regulation", {})
        
        update_response = {
            "response_id": f"RU_{datetime.now().strftime('%Y%m%d')}",
            "regulation_id": regulation.get("id"),
            "regulation_title": regulation.get("title", ""),
            "effective_date": regulation.get("effective_date"),
            "impact_assessment": {
                "affected_areas": ["投资流程", "风险管理", "报告要求"],
                "implementation_complexity": "中等",
                "cost_impact": "低",
                "timeline_impact": "无影响"
            },
            "implementation_plan": {
                "policy_updates": [
                    "修订投资政策",
                    "更新风险管理制度",
                    "完善操作流程"
                ],
                "system_changes": [
                    "更新合规检查规则",
                    "调整报告模板",
                    "增强监控功能"
                ],
                "training_requirements": [
                    "合规团队培训",
                    "业务团队宣贯",
                    "管理层汇报"
                ],
                "timeline": "3个月内完成"
            }
        }
        
        return {
            "status": "success",
            "update_response": update_response
        }
    
    async def _manage_audit(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """管理审计"""
        audit_type = task.get("audit_type", "internal")
        
        audit_management = {
            "audit_id": f"AM_{datetime.now().strftime('%Y%m%d')}",
            "audit_type": audit_type,
            "audit_scope": [
                "投资决策流程",
                "风险控制措施",
                "合规管理体系",
                "信息系统安全"
            ],
            "audit_timeline": {
                "preparation": "2周",
                "fieldwork": "4周",
                "reporting": "2周",
                "follow_up": "4周"
            },
            "audit_team": {
                "lead_auditor": "外部审计师",
                "internal_support": ["合规专员", "风险专员", "IT专员"],
                "management_liaison": self.agent_id
            },
            "deliverables": [
                "审计计划",
                "风险评估报告",
                "审计发现报告",
                "管理建议书",
                "整改跟踪报告"
            ]
        }
        
        return {
            "status": "success",
            "audit_management": audit_management
        }
    
    async def _review_policies(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """审查政策"""
        policy_review = {
            "review_id": f"PR_{datetime.now().strftime('%Y%m%d')}",
            "review_scope": "全面政策审查",
            "policies_reviewed": [
                "投资管理政策",
                "风险管理政策",
                "合规管理政策",
                "信息安全政策",
                "员工行为准则"
            ],
            "review_findings": {
                "outdated_policies": 2,
                "missing_policies": 1,
                "policy_gaps": 3,
                "inconsistencies": 1
            },
            "recommendations": [
                "更新投资管理政策以反映新监管要求",
                "制定ESG投资政策",
                "完善数据治理政策",
                "加强网络安全政策"
            ],
            "implementation_priority": {
                "high": ["ESG投资政策", "数据治理政策"],
                "medium": ["网络安全政策更新"],
                "low": ["流程优化建议"]
            }
        }
        
        return {
            "status": "success",
            "policy_review": policy_review
        }